Team members: Pearse Flood, Sam Dauenbaugh, Neil Nervekar, Allen Shufer

Abstract

CAPTCHA images are widely used as a verification method to make sure that the user is a human while performing various actions like making a new account or submitting a form. While this can be a very useful tool for security, our team wondered how accurately a machine learning model could read captchas, and thereby highlight vulnerable designs that should include greater complexity to avoid being susceptible to hacks. Two different datasets of captcha images from Kaggle were used to test the effectiveness of our model: one with color…

Neil Narvekar

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